In this case study, you’ll become the lead data scientist for an up-and-coming specialty coffee company seeking to use customer data to justify critically important business decisions. You will use scikitlearn to build four different decision tree models — two using entropy and two using gini impurity — to ascertain whether a potentially business-transforming deal with a mysterious coffee farm in China will take your business to the next level. The case study will involve your use of the full data science pipeline, from importing, loading and cleaning the data right through to modeling and concluding. In the case study, your decision trees will properly implement the supervised learning method of classification, and you will enforce the best practices of: making an appropriate train/test split one-hot encoding model evaluation restricting the maximum depth of the tree using random forest to increase predictive accuracy and control overfitting